Constrained latin hypercube sampling4/7/2024 ![]() Compared with other random or stratified sampling algorithms, LHS has a better space filling effect, better robustness, and better convergence character. Latin Hypercube Sampling (LHS) is one of the most popular sampling approaches, which is widely used in the fields of simulation experiment design, uncertainty analysis, adaptive metamodeling, reliability analysis, and probabilistic load flow analysis. These algorithms are illustrated by an example and applied to evaluating the sample means to demonstrate the effectiveness. Therefore, a general extension algorithm based on greedy algorithm is proposed to reduce the extension time, which cannot guarantee to contain the most original points. The basic general extension algorithm is proposed to reserve the most original points, but it costs too much time. The relationship of original sampling points in the new LHS structure is shown by a simple undirected acyclic graph. In order to get a strict LHS of larger size, some original points might be deleted. The extension algorithms start with an original LHS of size and construct a new LHS of size that contains the original points as many as possible. For reserving original sampling points to reduce the simulation runs, two general extension algorithms of Latin Hypercube Sampling (LHS) are proposed. ![]()
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